Abstract : Many Signal Processing problems may be posed as statistical parameter estimation problems. A desired solution for the statistical problem is obtained by maximizing the Likelihood(ML), the A-Posteriori probability (MAP) or by optimizing other criterion, depending on the a-priori knowledge. However, in many practical situations, the original signal processing problem may generate a complicated optimization problem e.g. when the observed signals are noisy and 'incomplete'. A framework of iterative procedures for maximizing the likelihood, the EM algorithm, is widely used in statistics. In the EM algorithm, the observations are considered 'incomplete' and the algorithm iterates between estimating the sufficient statistics of the 'complete data' given the observations and a current estimate of the parameters (the E step) and maximizing the likelihood of the complete data, using the estimated sufficient statistics (the M step). When this algorithm is applied to signal processing problems, it yields, in many cases, an intuitively appealing processing scheme.